Opioid Risk Assessment Tool for Opioid Use Disorder

(PharmTool Trial)

Recruiting at 1 trial location

Overseen By Nicole Mauerman, MS

Age: Any Age

Sex: Any

Trial Phase: Academic

Sponsor: University of Utah

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

80 Participants Needed

Trial Summary

Will I have to stop taking my current medications?

The trial does not specify whether participants need to stop taking their current medications. It focuses on pharmacies using a tool to assess opioid risk, not on individual patient medication changes.

What data supports the effectiveness of the treatment Clinical Decision Support Tool (High Risk) for Opioid Use Disorder?

Research shows that clinical decision support tools can improve primary care for opioid use disorder by increasing clinician confidence and accuracy in diagnosing and treating the condition. These tools help bridge the treatment gap for patients who need care but are not receiving it. ^{1 2 3 4 5}

Is the Opioid Risk Assessment Tool safe for use in humans?

The safety of the Opioid Risk Assessment Tool itself is not directly addressed in the available research, but there are concerns about the potential for clinical decision support systems to mislead clinicians and harm patients if not properly validated. ^{1 2 3 6 7}

How does the Opioid Risk Assessment Tool differ from other treatments for opioid use disorder?

The Opioid Risk Assessment Tool is unique because it helps healthcare providers predict the risk of opioid misuse by combining a standardized clinical examination with a validated risk assessment tool and, when needed, urine drug screening. This approach is more reliable than subjective assessments alone, making it a valuable tool in managing opioid use disorder. ^{1 2 4 7 8}

What is the purpose of this trial?

This goal of this observational study is to develop and test the Opioid Risk Reduction Clinical Decision Support (ORRCDS) tool. The tool will be an opioid medication risk screener and decision support platform that will be used by pharmacists upon dispensing prescription opioid medication. Once the Opioid Risk Reduction has been developed, we will examine the impact of the ORRCDS within two divisions of a large chain retail pharmacy. Pharmacies will be randomized to using the Opioid Risk Reduction Clinical Decision Support (ORRCDS) tool or standard of care opioid dispensation. We hypothesize that patients at pharmacies randomized to the ORRCDS tool will be more likely to reduce their risk status to low or moderate compared to the patients at standard of care pharmacies.

Research Team

Jerry Cochran, PhD

Principal Investigator

Associate Professor

Eligibility Criteria

This trial is for pharmacies in the Cincinnati or Columbus, Ohio areas. It's not for individual patients; instead, entire pharmacies will participate to see if a new tool helps manage opioid risks better than usual care.

Inclusion Criteria

A large chain retail pharmacy chain in the Cincinnati or Columbus, Ohio area

Exclusion Criteria

Not applicable.

Timeline

Tool Development

Development of the Opioid Risk Reduction Clinical Decision Support (ORRCDS) tool, including a universal opioid medication risk screener and decision support platform

24 months

Randomized Trial

Conduct a type-1 cluster 2-arm randomized trial to examine the impact of the ORRCDS tool within two divisions of a large chain retail pharmacy

24 months

Follow-up

Participants are monitored for sustainability and broader dissemination of the ORRCDS tool through qualitative interviews with pharmacists and leaders

24 months

Treatment Details

Interventions

• Clinical Decision Support Tool (High Risk)
• Clinical Decision Support Tool (Moderate Risk)
• Standard of Care

Trial Overview The study tests an Opioid Risk Reduction Clinical Decision Support (ORRCDS) tool against standard care in pharmacies. Pharmacies are randomly chosen to use either the ORRCDS tool or continue with their regular process when dispensing opioids.

Participant Groups

Group I: Intervention Pharmacy Site Experimental Treatment 2 Interventions

Pharmacies randomized to the experimental arm will be exposed to the intervention condition. Patients at the intervention pharmacy identified as at elevated risk will receive confirmatory screening for opioid risk. Those with confirmed moderate risk will receive a brief motivational intervention for medication misuse and an offer of naloxone dispensation. Those with high risk will receive a brief motivational intervention leading to warm handoff treatment linkage intervention to primary or specialty substance use care with an offer for naloxone dispensation.

Group II: Control Pharmacy Site Experimental Treatment 1 Intervention

Standard of Care is the treatment as usual condition, which follows federal and Ohio state pharmacy requirements for pharmacists where in patients filling prescriptions receive information and opt-in counseling. Ohio State law requires pharmacist to not dispense an opioid supply \>90 days, with a specific prohibition on dispensations ≥14 days after prescriptions are issued. Pharmacists are also required to perform a universal prescription drug medication review before initial dispensation and offer brief counseling (e.g., unstandardized information about medication safety) for new/modified prescription therapies.

Who Is Running the Clinical Trial?

University of Utah

Lead Sponsor

Trials

1,169

Recruited

1,623,000+

University of Cincinnati

Collaborator

Trials

442

Recruited

639,000+

National Institute on Drug Abuse (NIDA)

Collaborator

Trials

2,658

Recruited

3,409,000+

Findings from Research

A predictive model was developed and validated to estimate the risk of opioid-naive patients transitioning to long-term opioid use, based on a large cohort of over 7 million patients, showing high discrimination and calibration.

The model demonstrated a c-statistic of 0.904 in the development cohort and 0.913 in the validation cohort, indicating strong predictive performance, which could assist clinicians in making informed prescribing decisions to mitigate the risk of long-term opioid use.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Risk Prediction Model for Long-term Prescription Opioid Use. Tseregounis, IE., Tancredi, DJ., Stewart, SL., et al.[2023]

The OUD-CDS tool was successfully developed and demonstrated to be functional and accurate, helping primary care providers (PCPs) identify and manage patients at risk for opioid use disorder (OUD).

Although the tool improved PCP confidence in screening and diagnosing OUD, it was used in only 5% of encounters with at-risk patients, indicating a need for better integration into primary care practices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A pilot study of the functionality and clinician acceptance of a clinical decision support tool to improve primary care of opioid use disorder. Rossom, RC., Sperl-Hillen, JM., O'Connor, PJ., et al.[2021]

The study involved 1822 patients across 18 primary care clinics and found that nearly 47% of physicians used an algorithm-based decision tool to assess opioid use risk, which was rated beneficial for patient care by an average score of 3.6 out of 5.

Patients whose treatment was guided by this risk assessment tool experienced an average reduction of 2.7 levels in pain on a numeric rating scale, indicating that the tool effectively helped physicians make better treatment decisions, especially for minority patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Predictive Algorithm to Detect Opioid Use Disorder: What Is the Utility in a Primary Care Setting? Lee, C., Sharma, M., Kantorovich, S., et al.[2023]

In a study of 125 patients undergoing chronic opioid therapy, both patient-completed and physician-completed opioid risk tools (ORT) showed a moderate correlation (0.61), indicating some agreement in risk assessment.

However, neither version of the ORT effectively predicted moderate-to-severe aberrant drug-related behavior (ADRB), as a significant number of low, moderate, and high-risk patients still exhibited ADRB, suggesting limitations in the tool's clinical utility.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Usefulness of the opioid risk tool to predict aberrant drug-related behavior in patients receiving opioids for the treatment of chronic pain. Witkin, LR., Diskina, D., Fernandes, S., et al.[2022]

A new set of measurement-based care (MBC) tools, including the TAPS tool for universal screening and the OUD Symptom Checklist for symptom tracking, has been developed to support primary care physicians in treating opioid use disorder (OUD) with buprenorphine.

These tools aim to enhance treatment capacity for OUD by providing structured guidance during the first 12 weeks of buprenorphine treatment, although further testing is needed to evaluate their feasibility and acceptability in clinical practice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Creation of an algorithm for clinical decision support for treatment of opioid use disorder with buprenorphine in primary care. Dela Cruz, AM., Walker, R., Pipes, R., et al.[2021]

Clinical decision support (CDS) systems, especially those used for opioid prescribing, need rigorous validation to ensure they do not mislead clinicians and potentially harm patients, as highlighted by the risks associated with flawed predictions.

The paper emphasizes the importance of higher standards for clinical and technical validation of CDS systems, using the Stratification Tool for Opioid Risk Mitigation (STORM) and NarxCare as case studies to illustrate the need for improved oversight in predicting opioid-related adverse events.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A call for better validation of opioid overdose risk algorithms. McElfresh, DC., Chen, L., Oliva, E., et al.[2023]

Using validated risk assessment tools for opioid use disorder, along with standardized clinical examinations and urine drug screenings, significantly enhances the detection of opioid misuse compared to relying solely on caregiver assessments.

No single risk assessment tool has been found to be both highly reliable and sensitive, but a combined standardized approach is more effective in identifying potential opioid misuse.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Opioid Use Disorder Assessment Tools and Drug Screening. Ducharme, J., Moore, S.[2020]

The study identified 75 distinct variables from seven studies that can help predict opioid abuse, with medication variables being the most frequently mentioned, highlighting the importance of monitoring prescriptions.

Age and gender were consistently found to be significant demographic factors in predicting opioid abuse, suggesting that these characteristics should be considered in risk assessment tools.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models. Alzeer, AH., Jones, J., Bair, MJ.[2022]

References

1. United States pubmed.ncbi.nlm.nih.gov

A Risk Prediction Model for Long-term Prescription Opioid Use. [2023]

2. England pubmed.ncbi.nlm.nih.gov

A pilot study of the functionality and clinician acceptance of a clinical decision support tool to improve primary care of opioid use disorder. [2021]

3. United States pubmed.ncbi.nlm.nih.gov

A Predictive Algorithm to Detect Opioid Use Disorder: What Is the Utility in a Primary Care Setting? [2023]

4. United States pubmed.ncbi.nlm.nih.gov

Usefulness of the opioid risk tool to predict aberrant drug-related behavior in patients receiving opioids for the treatment of chronic pain. [2022]

5. England pubmed.ncbi.nlm.nih.gov

Creation of an algorithm for clinical decision support for treatment of opioid use disorder with buprenorphine in primary care. [2021]

6. England pubmed.ncbi.nlm.nih.gov

A call for better validation of opioid overdose risk algorithms. [2023]

7. United States pubmed.ncbi.nlm.nih.gov

Opioid Use Disorder Assessment Tools and Drug Screening. [2020]

8. England pubmed.ncbi.nlm.nih.gov

Review of Factors, Methods, and Outcome Definition in Designing Opioid Abuse Predictive Models. [2022]